PocketSphinx英文识别精度差及西班牙语模型集成问题求助

Hey there! Let's tackle your CMU Sphinx issues step by step—both boosting English recognition accuracy and setting up the Spanish model you downloaded.

Sphinx’s default English model is pretty basic, so tweaking it for your specific vocabulary will make a huge difference. Here’s what to do:

• Swap to a better acoustic model
  The default model is trained on generic speech, so use a more robust one like the WSJ or Hub4 models (designed for conversational/clear speech). Download the appropriate model, unzip it, and either replace the default Sphinx model folder or specify its path in your code.

• Ensure your audio matches Sphinx’s requirements
  Sphinx is picky about audio format—it only works with 16kHz, 16-bit, mono WAV files. If your input is in another format (like MP3), convert it first with a tool like FFmpeg:

  ffmpeg -i your_input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le converted_audio.wav
  

• Build a custom language model & dictionary for your target vocabulary
  This is the most impactful step for recognizing specific words. Here’s how:

  1. Create a text file (e.g., my_words.txt) with each word you need to recognize on a new line.
  2. Use Sphinx’s sphinx_lm_train tool to generate a custom language model (.lm file) tailored to your list.
  3. Generate a matching dictionary (.dict file) with sphinx_dict—this maps each word to its phonetic pronunciation. For rare words, you may need to look up their phonetics and add them manually.
  4. In your code, point recognize_sphinx to these custom files instead of the default ones:

     result = r.recognize_sphinx(
         audio,
         language_model_path="./my_custom_model.lm",
         dictionary_path="./my_custom_dict.dict"
     )
     

• Use keyword recognition mode
  Since you’re targeting specific words, skip general speech recognition and use Sphinx’s keyword mode. This prioritizes your target terms and reduces false positives:

  # Format: (keyword, threshold) — lower threshold = stricter recognition
  result = r.recognize_sphinx(
      audio,
      keyword_entries=[("your_target_word", 1e-50), ("another_word", 1e-50)]
  )
  

Setting up the Spanish model you downloaded is straightforward once you know where to point the code:

• Organize your model files
  Unzip the downloaded Spanish model package—you’ll find three key components:

  • Acoustic model folder (contains feat.params, mdef, means, etc.)
  • Language model file (.lm)
  • Dictionary file (.dict)
    Move all these into a dedicated folder, e.g., ./spanish_models/.

• Point your code to the Spanish models
  Instead of relying on the language parameter, manually specify the paths to the Spanish components in recognize_sphinx:

  from speech_recognition import Recognizer, AudioFile
  
  r = Recognizer()
  with AudioFile("spanish_audio.wav") as source:
      audio = r.record(source)
  
  # Recognize using Spanish models
  try:
      spanish_result = r.recognize_sphinx(
          audio,
          acoustic_model_path="./spanish_models/es-es",  # Path to your acoustic model folder
          language_model_path="./spanish_models/es-20k.lm",  # Path to LM file
          dictionary_path="./spanish_models/es.dict"  # Path to dict file
      )
      print("Spanish recognition result:", spanish_result)
  except:
      print("Could not recognize speech in Spanish")
  

• Verify audio format
  Just like with English, make sure your Spanish audio is 16kHz, 16-bit mono WAV—this is non-negotiable for good results.

• Customize for your specific Spanish vocabulary (optional)
  If your target words aren’t in the default Spanish dictionary, repeat the custom model/dictionary steps from the English section, but use Spanish phonetics for your terms.

内容的提问来源于stack exchange，提问作者majo